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文件名称:satish
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Avetis Ioannisyan
avetis@60ateight.com
Last Updated: 11/30/05
LMS Channel Adaptation
reset randomizers
randn( state ,sum(100*clock))
rand( state ,sum(100*clock))
numPoints = 5000
numTaps = 10 channel order
Mu = 0.001:0.001:0.01 iteration step size
input is guassian
x = randn(numPoints,1) + j*randn(numPoints,1)
choose channel to be random uniform
h = rand(numTaps, 1) + i*rand(numTaps, 1)
h = [1 0 0 0 1] testing only
h = h/max(h) normalize channel
convolve channel with the input
d = filter(h, 1, x)
initialize variables
w = []
y = []
in = []
e = [] error, f- Avetis Ioannisyan
avetis@60ateight.com
Last Updated: 11/30/05
LMS Channel Adaptation
reset randomizers
randn( state ,sum(100*clock))
rand( state ,sum(100*clock))
numPoints = 5000
numTaps = 10 channel order
Mu = 0.001:0.001:0.01 iteration step size
input is guassian
x = randn(numPoints,1) + j*randn(numPoints,1)
choose channel to be random uniform
h = rand(numTaps, 1) + i*rand(numTaps, 1)
h = [1 0 0 0 1] testing only
h = h/max(h) normalize channel
convolve channel with the input
d = filter(h, 1, x)
initialize variables
w = []
y = []
in = []
e = [] error, f
avetis@60ateight.com
Last Updated: 11/30/05
LMS Channel Adaptation
reset randomizers
randn( state ,sum(100*clock))
rand( state ,sum(100*clock))
numPoints = 5000
numTaps = 10 channel order
Mu = 0.001:0.001:0.01 iteration step size
input is guassian
x = randn(numPoints,1) + j*randn(numPoints,1)
choose channel to be random uniform
h = rand(numTaps, 1) + i*rand(numTaps, 1)
h = [1 0 0 0 1] testing only
h = h/max(h) normalize channel
convolve channel with the input
d = filter(h, 1, x)
initialize variables
w = []
y = []
in = []
e = [] error, f- Avetis Ioannisyan
avetis@60ateight.com
Last Updated: 11/30/05
LMS Channel Adaptation
reset randomizers
randn( state ,sum(100*clock))
rand( state ,sum(100*clock))
numPoints = 5000
numTaps = 10 channel order
Mu = 0.001:0.001:0.01 iteration step size
input is guassian
x = randn(numPoints,1) + j*randn(numPoints,1)
choose channel to be random uniform
h = rand(numTaps, 1) + i*rand(numTaps, 1)
h = [1 0 0 0 1] testing only
h = h/max(h) normalize channel
convolve channel with the input
d = filter(h, 1, x)
initialize variables
w = []
y = []
in = []
e = [] error, f
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